I want to add a liear layer after an encoder in VAE, to get an smaller latent space of a group of data, but the loss returns nan.
Does my idea have some problems? how to avoid the nan loss in the VAE model?
class FC_en(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(2429*32, 64)
self.BN1 = nn.BatchNorm1d(64)
def forward(self, x):
z_loc = self.BN1(self.fc1(x))
return z_loc
class FC_de(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(64,2429*32)
self.BN1 = nn.BatchNorm1d(2429*32)
def forward(self, z):
x = self.BN1(self.fc1(z))
return x
class VAE(nn.Module):
def __init__(self, z_dim=16, hidden_dim=1000, use_cuda=True):
super().__init__()
# create the encoder and decoder networks
self.encoder = Encoder(z_dim, hidden_dim)
self.decoder = Decoder(z_dim, hidden_dim)
self.fc3 = FC_en()
self.fc4 = FC_de()
if use_cuda:
# calling cuda() here will put all the parameters of
# the encoder and decoder networks into gpu memory
self.cuda()
self.use_cuda = use_cuda
self.z_dim = z_dim
# define the model p(x|z)p(z)
def model(self, x):
# register PyTorch module `decoder` with Pyro
pyro.module("decoder", self.decoder)
with pyro.plate("data", x.shape[0]):
# setup hyperparameters for prior p(z)
z_loc = x.new_zeros(torch.Size((x.shape[0], self.z_dim)))
z_scale = x.new_ones(torch.Size((x.shape[0], self.z_dim)))
# sample from prior (value will be sampled by guide when computing the ELBO)
z = pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))
# decode the latent code z
loc_img = self.decoder(z)
loc_img = loc_img.reshape(-1,200*200)
pyro.sample("obs", dist.Bernoulli(loc_img).to_event(1), obs=x.reshape(-1, 200*200))
# define the guide (i.e. variational distribution) q(z|x)
def guide(self, x):
# register PyTorch module `encoder` with Pyro
pyro.module("encoder", self.encoder)
with pyro.plate("data", x.shape[0]):
# use the encoder to get the parameters used to define q(z|x)
z_loc, z_scale = self.encoder(x)
z_sum = torch.cat((z_loc,z_scale),1)
z_sum = z_sum.view(2, 2429*32)
z_sum_z = self.fc3(z_sum)
loc_img = self.fc4(z_sum_z)
loc_img = loc_img.reshape(2429*2, 32)
z_loc = loc_img[:, 0:16]
z_scale = loc_img[:, 16:32]
# sample the latent code z
pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))
